I'm going to describe a complete pipeline for Traffic Sign Recognition problem posed in Udacity course "Self-Driving Cars Engineer". Traffic Sign Recognition is a basic, day by day task for self-driving cars. That's why it has to be covered in the series about Self-Driving Cars where I present different projects related to this field. The recognition system processes a traffic sign image extracted from the road scene. Eventually, it should classify that sign into one of 43 categories. In order to make it happen, a Convolutional Neural Network is applied, being trained with 50.000 images beforehand.More Traffic Sign Recognition using Convolutional Neural Network

Hough Lines Transform is the key method used in the previous project where lane lines are detected. It is very helpful in many Computer Vision applications. The original form of Hough Transform aimed to identify straight lines. And that's what I'm going to explain today. Furthermore, this technique was later generalized to detect also other shapes like circles, ellipses etc. [1].

Although Deep Neural Networks play bigger and bigger role in scene recognition, classic Computer Vision methods are still valid and are applied to problems from autonomous cars industry. I'm going to present how to perform lane lines detection using OpenCV library and Python language using image processing techniques. It's the first project in the series about Self-Driving Cars.More First step to stay on track - lane lines detection

Isn't it beautiful that having only a computer, internet connection and some software skills you can learn and develop things which relate to the most cutting-edge technologies today? And I don't mean only pure software business like web services, social media or mobile apps. With a bit of dedication and persistence you can enter to the world of robotics, AI, VR or... Self-Driving Cars.More Self-Driving Cars in Python

This time we are going to discuss the influence of two basic variables on the quality of SVM classifier. They are called hyperparameters to distinguish them from the parameters optimized in a machine learning procedures. Two previous posts introduced Support Vector Machine itself and data preprocessing for this classifier. As in other Machine Learning techniques there is also a need to properly adjust some system variables to find the best model for our needs. Here, we will focus on description of complexity parameter and gamma parameter from the Gaussian kernel. In the next article we will find an optimum SVM model for the foreground/background estimation problem in Flover project using model validation techniques.More SVM model selection - how to adjust all these knobs pt. 1

Data preprocessing is a very important and quite underestimated step in Machine Learning pipelines. It provides cleaned and relevant datasets which then can be used in further steps like classification or regression. I will describe a study case for data which is fed to the SVM classifier to predict if a given image segment belongs to foreground or background. This is a second article about Support Vector Machine which is used for image segmentation in my flower species recognition project Flover.